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This paper presents a new method for Estimate at Completion (EAC) of project's time to improve earned value management system (EVMS). The new formula established consists of four variables: Scheduled Performance Index (SPI), Scheduled Percent Complete by Duration (SPCD), Actual Percent Complete by Duration (APCD), and Sum of Durations to Due Time (SDDT). The model has been validated by a simulation study using a progress generator program for typical RCPS problem project library. This program was developed in VBA-MSP and it included some basic assumptions and data were generated day by day randomly. The results show a strong linear relationship between response variable and above four predictors. Therefore, it can be concluded that the model could be used in a wide range of projects for EAC estimation.

In response to price dispersion across stores and price promotions over time, consumers search across both stores and time, in many retail settings. Yet there is no search model in extant research that jointly endogenizes search in both dimensions. We develop a model of search across stores and across time that nests a finite horizon model of search across stores within an infinite horizon model of inter-temporal search. The model is estimated using an iterative procedure that formulates it as a mathematical program with equilibrium constraints (MPEC) embedded within an E-M algorithm to allow estimation of latent class heterogeneity.The empirical analysis uses data on household store visits and purchases in the milk category. In contrast to extant research, we find that omitting the temporal dimension underestimates price elasticity. We attribute this difference to the relative frequency of household stockouts and purchase frequency in the milk category.Interestingly, we find that for a given mean and variance in price, increasing the frequency while reducing depth of price promotions across all stores can increase share of visits and profits for consumers' preferred store for the segment with high price sensitivity and low cross-store search cost; consumers who are most prone to search across stores and across time.

In retail settings with price promotions, consumers often search across stores and time. However, the search literature typically only models one pass search across stores, ignoring revisits to stores; the choice literature using scanner data has modeled search across time, but not search across stores in the same model. We develop a multipass search model that jointly endogenizes search in both dimensions; our model nests a finite horizon model of search across stores within an infinite horizon model of intertemporal search. We apply our model to milk purchases at grocery stores; hence, the model also accounts for repeat purchases across time, inventory holding by households, and grocery basket effects. We note that the special case without these additional features can be used to study one-time purchases with repeat store visits as in the case of durable goods and online shopping. We formulate the empirical model as a mathematical program with equilibrium constraints and estimate it allowing for latent class heterogeneity using an iterative expectation-maximization algorithm. In contrast to extant research, we find that omitting the temporal dimension underestimates price elasticity. We attribute this difference to the relative frequency of household stockouts and purchase frequency in the milk category. Interestingly, increasing the promotional frequency (while reducing its depth to maintain the mean and variance of prices across all stores) can increase loyalty to the household’s preferred store. This paper was accepted by Juanjuan Zhang, marketing.